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Integrated Sensing and Communication Waveform Design Through Exploiting Both Spatial-Temporal Interference

  • Beijing Institute of Technology
  • Southeast University, Nanjing
  • University College London

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we investigate the spatial-temporal constructive interference (STCI) based waveform design for multiuser multiple-input single-output (MU-MISO) integrated sensing and communication (ISAC) systems. By leveraging the concept of constructive interference (CI) and designing a novel STCI prefix, the temporal inter-symbol interference (ISI) and spatial multi-user interference (MUI) are jointly exploited to enhance the reliability of ISAC transmission. In particular, we first formulate the quality-of-service (QoS) balancing oriented waveform design problem, where the worst communication QoS among user equipments (UEs) is maximized while satisfying the Cramér-Rao bound (CRB) for target angle estimation. To tackle the non-convex problem efficiently, a successive convex approximation-Karush-Kuhn-Tucker conditions-majorization minimization (SCA-KKT-MM) framework is proposed, which is further utilized to tackle the power minimization oriented waveform design problem. Extensive simulation results are presented to validate its effectiveness, where the symbol error rate (SER), root mean square error (RMSE), transmit beampattern and transmit power of the proposed algorithms are compared with the prior arts, showing that the proposed STCI-based waveform outperforms the conventional zero-forcing precoded orthogonal frequency division multiplexing (OFDM) waveform and spatial CI-based symbol-level precoded OFDM waveform in terms of both communication SER and RMSE in target angle estimation.

Original languageEnglish
Pages (from-to)18609-18624
Number of pages16
JournalIEEE Transactions on Wireless Communications
Volume25
DOIs
Publication statusPublished - 2026
Externally publishedYes

Keywords

  • constructive interference
  • Integrated sensing and communication
  • majorization minimization
  • symbol-level precoding

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